Sequence data pattern mining Framework in Java (SPMF)

I was doing some research on how to mine patterns from sequence data and I found a really good open sourced platform called SPMF.

SPMF is an open-source data mining platform written in Java. It is distributed under the GPL v3 license.

SPMF is an open-source data mining mining platform written in Java.

The link to the website:  http://www.philippe-fournier-viger.com/spmf/index.php

It offers implementations of 52 data mining algorithms for:

• sequential pattern mining,
• association rule mining,
• frequent itemset mining,
• sequential rule mining,
• clustering

It can be used as a standalone program with a user interface or from the command line. Moreover, the source code of each algorithm can be integrated in other Java software.

The following picture is a map which you can visualize the relationship between the various data mining algorithms offered in SPMF.

Visual map of algorithms

Supporting Algorithms

Sequential Pattern Mining Algorithms

• the PrefixSpan algorithm for mining frequent sequential patterns from a sequence database (Pei et al., 2004).
• the SPAM algorithm for mining frequent sequential patterns from a sequence database (Ayres, 2002)
• the BIDE+ algorithm for mining frequent closed sequential patterns from a sequence database (Wang et al. 2007)
• the SeqDIM algorithm for mining frequent multidimensional sequential patterns from a multi-dimensional sequence database (Pinto et al. 2001)
• the Songram et al. algorithm for mining frequent closed multidimensional sequential patterns from a multi-dimensional sequence database (Songram et al. 2006)
• the Fournier-Viger et al. algorithm, a sequential pattern mining algorithm that combines several features from well-known sequential pattern mining algorithms and also proposes some original features (Fournier-Viger et al., 2008):
  • mining sequences with minimum support by database-projection (based on PrefixSpan, Pei et al., 2004)
  • mining sequences with min/max time interval between events and min/max time length of a sequence (based on Hirate-Yamana, 2006)
  • mining closed sequences (based on the BIDE+ algorithm by Wang et al. 2007)
  • mining multi-dimensional sequences (based on Pinto et al. 2001)
  • mining closed multi-dimensional sequences (based on Songram et al. 2006 and Pasquier et al., 1999)
  • mining sequences with items having integer values and performing automatic clustering of these values (original extension described in Fournier-Viger et al., 2008)

Sequential Rule Mining Algorithms

• the RuleGrowth algorithm for mining sequential rules from a sequence database (Fournier-Viger et al., 2011, powerpoint)
• the TRuleGrowth algorithm for mining sequential rules from a sequence database with the window size constraint (Fournier-Viger, 2012a)
• the CMRules algorithm for mining sequential rules from a sequence database (Fournier-Viger et al., 2010, powerpoint)
• the CMDeo algorithm for mining sequential rules from a sequence database(Fournier-Viger et al., 2010)
• the TopSeqRules algorithm for mining the top-k sequential rules from a sequence database (Fournier-Viger et al., 2011, powerpoint)
• the RuleGen algorithm for mining sequential rules from a sequence database (Zaki et al, 2001)
• the TNS algorithm for mining top-k non-redundant sequential rules (Fournier-Viger 2012c) 

Frequent Itemset Mining Algorithms

• the Apriori algorithm for discovering frequent itemsets from a transaction database. (Agrawal & Srikant, 1994)
• the AprioriTID algorithm for discovering frequent itemsets from a transaction database. (Agrawal & Srikant, 1994)
• the FP-Growth algorithm for discovering frequent itemsets from a transaction database. (Han et al., 2004)
• the Eclat algorithm for discovering frequent itemsets from a transaction database. (Zaki, 2000)
• the Relim algorithm for discovering frequent itemsets from a transaction database (Borgelt, 2005)
• the H-Mine algorithm for discovering frequent itemsets from a transaction database (Pei et al., 2007)
• the Charm algorithm for discovering frequent closed itemsets from a transaction database (Zaki and Hsiao, 2002)
• the DCI_Closed algorithm for mining frequent closed itemsets from a transaction database (Lucchese et al, 2004)
• the AprioriClose a.k.a Close algorithm for discovering frequent itemsets and frequent closed itemsets from a transaction database (Pasquier et al., 1999)
• the AprioriTIDClose algorithm for discovering frequent itemsets and frequent closed itemsets from a transaction database (Pasquier et al., 1999, Agrawal & Srikant, 1994)
• the Charm-MFI algorithm for discovering frequent closed itemsets and maximal frequent itemsets from a transaction database (Szathmary et al. 2006)
• the AprioriInverse algorithm for mining all perfectly rare itemsets from a transaction database (Koh & Roundtree, 2005)
• the AprioriRare algorithm for mining minimal rare itemsets and frequent itemsets from a transaction database (Szathmary et al. 2007b)
• the Zart algorithm for discovering frequent closed itemsets and their minimal generators from a transaction database (Szathmary et al. 200)
• the U-Apriori algorithm for mining frequent itemsets from uncertain data (Chui et al, 2007)
• an algorithm for mining pseudo-closed itemsets from a transaction database (Pasquier et al., 1999)
• the CloStream algorithm for mining frequent closed itemsets from a data stream (Yen et al, 2009)
• the Two-Phase algorithm for mining high-utility itemsets from a transaction database with utility information (Liu et al., 2005)
• the HUI-Miner algorithm for mining high-utility itemsets from a transaction database with utility information (Liu & Qu, CIKM 2012) 
• the VME algorithm for mining ereasable itemsets (Deng & Xu, 2010)
• algorithms for building, updating and querying an Itemset-Tree, a structure that allows efficiently generating targeted association rules and frequent itemsets and can be updated incrementally.(Kubat et al, 2003)
• the MSApriori algorithm for mining frequent itemsets with multiple minimum supports (Liu et al, 1999) 
• the CFPGrowth algorithm for mining frequent itemsets with multiple minimum supports (Hu & Chen, 2006) 

Association Rule Mining Algorithms

• an algorithm for mining all association rules from a transaction database (Agrawal & Srikant, 1994)
• an algorithm for mining all association rules with the lift measure from a transaction database (adapted from Agrawal & Srikant, 1994)
• an algorithm for mining the IGB informative and generic basis of association rules from a transaction database (Gasmi et al., 2005)
• an algorithm for mining perfectly sporadic association rules (Koh & Roundtree, 2005)
• an algorithm for mining the Guigues-Duquenne basis for exact association rules from a transaction database (Pasquier et al., 1999)
• an algorithm for mining the proper basis for approximative association rules from a transaction database (Pasquier et al., 1999)
• an algorithm for mining the structural basis for approximative association rules from a transaction database (Pasquier et al., 1999)
• an algorithm for mining closed association rules (Szathmary et al. 2006).
• an algorithm for mining minimal non redundant association rules (Kryszkiewicz, 1998)
• the Indirect algorithm for mining indirect association rules (Tan et al. 2000; Tan et 2006)
• the FHSAR algorithm for hiding sensitive association rules (Weng et al. 2008)
• the TopKRules algorithm for mining the top-k association rules (Fournier-Viger, 2012b, powerpoint)
• the TNR algorithm for mining top-k non-redundant association rules (Fournier-Viger 2012d, powerpoint)

Clustering Algorithms

• the K-Means algorithm for clustering vectors containing one or more double values (MacQueen, 1967)
• a hierarchical clustering algorithm

Classification Algorithms

• the ID3 algorithm for building decision trees (Quinlan, 1986)

Data structures

• red-black tree,
• itemset-tree,
• binary tree,
• KD-tree,
• triangular matrix.